This disclosure relates to the measurement, aggregation and analysis of data collected using non-contact or minimal-contact sensors together with a means for capturing subjective responses to provide quality of life parameters for individual subjects, particularly in the context of a controlled trial of interventions on human subjects (e.g., a clinical trial of a drug, or an evaluation of a consumer item such as a fragrance).
Monitoring of quality-of-life (QOL) parameters can be of importance when developing interventions aimed at improving a person's QOL. Quality-of-life parameters are measurements of general well-being which are generally accepted as being meaningful to an individual's perception of their life. In general QOL markers have a combination of an underlying objectively measurable elements, and a subjectively related element. Specific non-limiting examples include:                Sleep quality—an individual can subjectively report whether they are sleeping well or badly, and this has an impact on their perceived QOL. For a sleep quality QOL parameter, an objective measurement could be sleep duration, and a subjective input could be “how restful” the sleep was.        Stress—an individual can report on whether they find their current life circumstances to be stressful. For a stress QOL parameter, an objective measurement could be heart rate or cortisol levels; a subjective element could be a stress level questionnaire        Relaxation—an individual can report the subjective sensation of being relaxed, which can also be objectively related to autonomic nervous system activity.        Pain—an individual can subjectively record levels of pain using a Pain Index [such as the Visual Analog Scale]. More objective measurements of pain can be obtained using a dolorimeter        Body temperature—subjects can often report feelings of overheating or coolness which are not directly related to objective measurement of body core temperature.        Vigilance/drowsiness—vigilance, or attentiveness can also be measured objectively (e.g., using the psychomotor vigilance test) or through subjective questionnaires.        
For clarification, a non-contact (or contactless) sensor is one which senses a parameter of a subject's physiology or behavior without any direct physical contact with a subject. Non-limiting examples include a movement detector based on radio-wave reflections, a microphone placed remotely from a subject, an infrared camera recording the surface temperature, or a faucet-monitor which records turning on of a faucet to wash hands. A minimal contact sensor may be considered to be one in which there is some physical contact with a sensor, but this is limited to short durations. Examples include a weight scale, a blood pressure monitor, a breath analyzer, or a hand-held surface ECG monitor. These minimal contact sensors can be distinguished from contact sensors typically used in clinical trials such as ECG patches, oximeters, EEG electrodes, etc, where there typically is adhesion to the body, and typically the sensor is intended for use over prolonged periods of time (e.g. >1 hour).
A key unifying factor in defining QOL parameters is the need to combine objective data from sensors, and subjective data from the monitored subject to assess the overall QOL. A particular challenge then arises when one wishes to measure the impact of an intervention on changes in QOL. For example, a company who has developed a drug to counteract sleep disruption will be interested to see if its drug has had any direct impact on a person's sleep which has resulted in either objectively or subjectively improved QOL. Similarly if a company has developed a product such as a skin emollient to reduce itchiness due to dry skin, they may wish to see if there has been an improved QOL (i.e., reduced scratching, lower level of discomfort) etc.
One commonly accepted means for answering such questions is to conduct a clinical or consumer trial which poses a statistical hypothesis which can be verified or rejected with a certain level of confidence. For example, in drug trials a double-blinded random controlled trial is a well accepted methodology for ascertaining the effect of drugs. However, measurement of QOL is difficult to conduct for a number of reasons, which various aspects of this disclosure can overcome: (a) it can be difficult to define a suitable measure for a QOL outcome, (b) by wearing a measurement device to measure QOL, one may directly impact on the exact quality-of-life parameter you wish to study, (c) there are logistical and financial challenges of measuring parameters in a natural “home” setting rather than in a formal laboratory setting. There are a variety of conventional techniques to measure some aspects of QOL which will now be discussed, together with their limitations.
Monitoring of a quality-of-life parameter can be motivated by a desire to integrate it into an intervention program. As an example, a person may undertake cognitive behavioral therapy (CBT) to reduce their stress-related quality of life. An important component of a CBT program is the ongoing assessment of the stress quality of life index, whose measurement will itself form part of the behavioral intervention. As a second example of an embodiment of the disclosure, we will describe a system for improving sleep quality through use of objective and subjective measurements of sleep quality-of-life indices.
As specific examples of the limitations of the current state of the art, consider the problem of measuring sleep quality in response to an anti-insomnia drug. Firstly, defining “sleep quality” as it relates to quality of life can be difficult, as this will often be a combination of objective and subjective measurements. Secondly, the current method favored for measuring sleep is to use a so-called polysomnogram which measures multiple physiological parameters (EEG, ECG, EOG, respiratory effort, oxygen level etc.). While the resulting physiological measurements are very rich, their measurement fundamentally alters the sleeping state of the subject (e.g., it is harder for them to turn over in bed), and cannot represent a true QOL sleep measurement. Finally, the current cost of the polysomnogram test (approximately $1500 in 2008) makes it an impractical tool for measurement of sleep quality in large numbers of subjects over long periods of time. Accordingly, there is a need for a system which can provide robust measurements of sleep quality-of-life in a highly non-invasive fashion. In an embodiment of our system, we describe one method for objectively measuring sleep quality using a totally non-invasive biomotion sensor. This can be combined with a number of subjective tools for measuring sleep quality, such as the Pittsburgh Sleep Quality Index and the Insomnia Severity Index (these consist of questionnaires on sleep habits such as time-to-bed, estimated time-to-fall-asleep etc.
Another QOL parameter of interest is stress level or, conversely, relaxation. Current techniques for objective measurement of stress include measurement of heart rate variability or cortisol levels. However, measurement of heart rate variability typically requires the subject to wear electrodes on the chest, which is often impractical for situations of daily living. Likewise, collection of cortisol samples to assess stress requires frequent collection of saliva samples, and is difficult to integrate into a daily living routine. There are also a number of widely used subjective measurements of stress or anxiety (e.g., Spielberger's State-Trait Anxiety Inventory). Accordingly, a method, system or apparatus which can reliably gather information about stress-related QOL parameters would have utility in a variety of settings.
Finally, measurement of the quality-of-life implications of chronic pain (such as chronic lower back pain) would have utility for assessing the benefit of therapies, or for providing cognitive feedback on pain management. Current subjective measurement tools such as the Oswestry Disability Index and the 36-Item Short-Form Health Survey are used to assess subjective quality of life in subjects with chronic pain conditions. Objective measurements of pain are not well defined, but there is some evidence that heart rate is correlated with pain intensity.
Accordingly, there is a clearly established need for systems and methods which measure quality-of-life outcomes in ambulatory/home settings, and which have minimal impact on the daily routine of the person whose QOL is being monitored. This is a particular need in clinical trials for non-contact or minimal contact sensors where the effects of interventions such as drugs, ointments, physiotherapy, nutriceuticals, behavior changes etc. are being evaluated.